Emerald AI

emeraldai.co ↗

Retrieved sources

Emerald AI — https://emeraldai.co/

Emerald AI was founded in 2024 and is headquartered in Washington, D.C. ^[1] It develops software that turns energy-intensive AI data centers into flexible assets for the electricity grid. This fits into Smart Grids ^[1]

Value Proposition & Features

Emerald AI’s value proposition is to help AI data centers obtain or use grid capacity without treating their electricity demand as fixed. Its Emerald Conductor platform adjusts computing demand in response to grid conditions while maintaining required compute performance. ^{[1] [2]}

Core features include real-time coordination between data-center workloads and electricity-grid signals. Lower-priority work can be slowed or rescheduled while higher-priority inference and other critical services continue operating. ^[2]

The platform also coordinates workloads across data centers and can incorporate onsite energy resources, allowing facilities to reduce or shift power draw during grid stress. ^{[1] [3]}

• Grid-responsive workload orchestration for AI data centers. ^{[1] [2]}

• Workload prioritization that protects critical inference and other high-priority services. ^[2]

• Dynamic power reduction during periods of grid constraint. ^{[1] [2]}

• Scheduling and shifting of flexible compute across time or locations. ^{[4] [5]}

• Coordination across multiple data centers as a distributed compute network. ^[1]

• Integration with onsite energy resources to manage facility power demand. ^[3]

• Utility-facing grid flexibility, including demonstrations with Silicon Valley Power. ^[2]

The approach fits naturally within the AI Factories concept because it treats the data center as both a compute facility and a controllable energy asset.

Screenshots

No three official public screenshots were identified in the retrieved results.

Product Roadmap / Announcements

As of October 4, 2026,

• September 17, 2026: Emerald AI was reported as participating in an alliance with Google, NVIDIA, and Anthropic to expand grid capacity for AI data centers. ^[6]

• September 15, 2026: NVIDIA described Conductor as an early example of grid flexibility for AI factories and reported a demonstration in which power fell from four megawatts to three while high-priority inference continued. ^[2]

• August 2026: Emerald AI announced a $150 million Series A intended to support broader deployment and development of Conductor. ^{[6] [7]}

Recent Developments

Within the 90-day period ending October 4, 2026, Emerald AI raised a $150 million Series A led by Energize Capital and DCVC at a reported valuation of approximately $1.05 billion. ^{[6] [5]} The company also received increased public attention through its work with Google, NVIDIA, and Anthropic on expanding AI data-center capacity without proportionally increasing grid strain. ^[6]

History and Origin Story

Emerald AI was founded in 2024 by Varun Sivaram, who serves as founder and CEO, and Ayse Coskun, a professor identified in company coverage as a co-founder. ^{[4] [8]} Its central premise is that software can make AI workloads responsive to changing electricity-system conditions, converting data centers from inflexible loads into grid-supporting resources. ^{[1] [2]}

Fundraising History

The retrieved entity data reports total funding of $216,519,200, while other coverage reports more than $220 million raised after the Series A; the totals differ by source and likely reflect different treatment of corporate and strategic rounds. ^{[1] [4]}

DCVC
Energize Capital
General Catalyst
NVIDIA
Salesforce Ventures
Samsung Ventures
Energize Capital and DCVC led the Series A. ^[6] Other reported participants include NVIDIA, Salesforce Ventures, General Catalyst, Samsung Ventures, Emerson Collective, and additional strategic investors. ^[10]

Notable Team Members

Varun Sivaram is Emerald AI’s founder and CEO. ^[4] He is described in coverage as a former climate-policy adviser whose background aligns with the company’s focus on electricity systems and decarbonization. ^[8]

Ayse Coskun is identified in company coverage as a co-founder and professor. ^[8] The retrieved results do not provide a fuller current executive biography.

Market Sizing

Category, Market Size, and Category Growth

Emerald AI operates at the intersection of AI data-center infrastructure, grid-interactive computing, and energy-management software. Its immediate market opportunity is driven by the need to connect rapidly growing AI electricity demand with constrained grid capacity. ^{[6] [2]}

A precise independent market-size estimate for Emerald AI’s specific category was not provided in the retrieved results. One company post claimed that its approach could unlock up to 100 gigawatts of otherwise untapped U.S. grid capacity, but that figure is a company-attributed potential estimate rather than an independently validated market-size measurement. ^[4]

Pricing

Revenue Trajectory Estimates

No reliable public revenue or ARR estimate was found in the retrieved results.

Competitive Landscape

Who it's for, who it's not for

Emerald AI is aimed at large AI data-center operators, utilities, global infrastructure providers, and AI laboratories that need to manage multi-megawatt computing loads against changing grid conditions. Its reported customer and partner profile includes leading AI labs, major utilities, and global data-center operators. ^[1]

It is not primarily designed for small businesses, ordinary cloud-software buyers, or organizations without large controllable compute loads and meaningful electricity constraints.

Viable Alternatives

• NVIDIA DSX Flex: Provides a related approach to flexible AI-factory power management and is described by NVIDIA as a platform category that Conductor helps demonstrate. ^[2]

• Traditional data-center demand-response systems: Existing utility programs can reduce facility load, but generally lack Emerald AI’s workload-level orchestration focus.

• Battery and onsite-generation systems: These manage grid exposure through physical energy assets rather than primarily shifting AI computation.

• Cloud-provider workload schedulers: These can move or defer compute jobs, but typically do not specialize in coordinating AI workloads with real-time regional grid signals.

Competitor Table

Sources

[1]: Emerald AI: 7 Jobs, Salaries & Tech Stack (2026) | Alion

Sources (retrieved)